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• W3131650997 abstract "Bi-factor and second-order models based on copulas are proposed for item response data, where the items are sampled from identified subdomains of some larger domain such that there is a homogeneous dependence within each domain. Our general models include the Gaussian bi-factor and second-order models as special cases and can lead to more probability in the joint upper or lower tail compared with the Gaussian bi-factor and second-order models. Details on maximum likelihood estimation of parameters for the bi-factor and second-order copula models are given, as well as model selection and goodness-of-fit techniques. Our general methodology is demonstrated with an extensive simulation study and illustrated for the Toronto Alexithymia Scale. Our studies suggest that there can be a substantial improvement over the Gaussian bi-factor and second-order models both conceptually, as the items can have interpretations of discretized maxima/minima or mixtures of discretized means in comparison with discretized means, and in fit to data." @default.
• W3131650997 created "2021-03-01" @default.
• W3131650997 creator A5037713486 @default.
• W3131650997 creator A5051053502 @default.
• W3131650997 date "2022-11-21" @default.
• W3131650997 modified "2023-09-26" @default.
• W3131650997 title "Bi-factor and Second-Order Copula Models for Item Response Data" @default.
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• W3131650997 doi "https://doi.org/10.1007/s11336-022-09894-2" @default.
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• W3131650997 hasPublicationYear "2022" @default.
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